| Sumario: | The role of heart disease prediction in healthcare is especially important, as it allows taking timely actions and making informed decisions. This paper has proposed an Enhanced Deep Learning-based Heart Disease Prediction Model (DL-HDP) that integrates optimization via Particle Swarm Optimization (PSO). The model is based on the deep learning model, namely Multilayer Perceptron (MLP), to classify heart disease with the use critical medical features like age, blood pressure, cholesterol levels, and ECG results. It uses PSO optimization algorithm to tune hyperparameters and select features and optimizes hyperparameters to tune the number of neurons, learning rates, and activation functions to improve the performance of the model. The performance of DL-HDP on experimental data proves that it is much more accurate, precise, and recalls than any traditional machine learning algorithms, proving the potency of deep learning with nature-inspired optimization. This is a valid method that has resulted in a reliable and effective tool in identifying heart disease early on, which is in aid of informing better clinical practice.
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